Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm

By Nameirakpam Dhanachandra, Khumanthem Manglem, Y. J. Chanu. 2015

1,025 citations recorded by the source corpus.

Abstract Image segmentation is the classification of an image into different groups. Many researches have been done in the area of image segmentation using clustering. There are different methods and one of the most popular methods is k-means clustering algorithm. K -means clustering algorithm is an unsupervised algorithm and it is used to segment the interest area from the background. But before applying K -means algorithm, first partial stretching enhancement is applied to the image to improve the quality of the image. Subtractive clustering method is data clustering method where it generates the centroid based on the potential value of the data points. So subtractive cluster is used to generate the initial centers and these centers are used in k-means algorithm for the segmentation of image. Then finally medial filter is applied to the segmented image to remove any unwanted region from the image.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/471be68bbf1d9cc9c8e9f0b65bd3965f1d99590c

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In Computer Vision and Pattern Recognition

Citations in the corpus

Image Segmentation Using Deep Learning: A Survey (2020, methodology): “Numerous image segmentation algorithms have been developed in the literature, from the earliest methods, such as thresholding [4], histogram-based bundling, regiongrowing [5], k-means clustering [6], watershed methods [7], to more advanced algorithms such as active contours [8], graph cuts [9], cond”